  Prandtl Number Effects on Extreme Mixing Events in Forced Stratified Turbulence
Bibliographic record
Abstract
`Strongly' stratified turbulent flows can self-organise into a `layered anisotropic stratified turbulence' (LAST) regime, characterised by relatively deep and well-mixed density `layers' separated by relatively thin `interfaces' of enhanced density gradient. Understanding the associated mixing dynamics is important for parameterising heat transport in the world's oceans. It is challenging to study `LAST' mixing, as it is associated with Reynolds numbers Re := UL/ν >> 1 and Froude numbers Fr :=(2πU)/(L N) > 1 where ε is the TKE dissipation rate. This requirement is exacerbated for oceanically relevant flows, as the Prandtl number Pr := ν /κ = O(10) in thermally-stratified water (where κ is the thermal diffusivity), thus leading (potentially) to even finer density field structures. We report here on four forced fully resolved direct numerical simulations of stratified turbulence at various Froude (Fr=0.5, 2) and Prandtl numbers (Pr=1, 7) forced so that Reb=50, with resolutions up to 30240 x 30240 x 3780. We find that, as Pr increases, emergent `interfaces' become finer and their contribution to bulk mixing characteristics decreases at the expense of the small-scale density structures populating the well-mixed `layers'. Nevertheless, `extreme' mixing events (with elevated local destruction rates of buoyancy variance χ0 dominating the total mixing budget) are still preferentially found in strongly stratified interfaces, which has significant implications for parameterising diapycnal mixing in larger scale ocean models. This project received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant Agreement No. 956457 and used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. S.deB.K. was supported under U.S. Office of Naval Research Grant number N00014-19-1-2152.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".